Deep Learning

This advanced course introduces students to deep learning, a rapidly growing field that focuses on learning from large volumes of data using extensive neural networks. The four-module curriculum begins with neural network fundamentals, exploring their structure and components while drawing parallels to biological neural networks. Students learn how to program these networks from the ground up. In the second module, students master practical implementation using TensorFlow and Keras, two powerful libraries for building and training neural networks. They explore various applications of these frameworks for deep learning tasks. The third module focuses on optimization techniques for neural networks, teaching students how to build more robust and effective learning systems. In the final module, students apply their knowledge to create a recurrent neural network for text generation based on an author's writing style. This practical project integrates concepts from previous modules into a real-world application. Through hands-on programming exercises and practical applications, students gain comprehensive knowledge of deep learning foundations, implementation strategies, and optimization techniques. By course completion, participants will have the skills to build sophisticated neural network systems for complex pattern recognition and generation tasks.